Mobile communications networks are in the process of offering increasingly sophisticated capabilities associated with the motion and/or position location sensing of a mobile device. New software applications, such as, for example, those related to personal productivity, collaborative communications, social networking, and/or data acquisition, may utilize motion and/or position sensors to provide new features and services to consumers. Moreover, some regulatory requirements of various jurisdictions may require a network operator to report the location of a mobile device when the mobile device places a call to an emergency service, such as a 911 call in the United States.
Such motion and/or position determination capabilities have conventionally been provided using digital cellular positioning techniques and/or Satellite Positioning Systems (SPS). Additionally, with the increasing proliferation of miniaturized motion sensors (e.g., simple switches, accelerometers, angle sensors, etc), such on-board devices may be used to provide relative position, velocity, acceleration, and/or orientation information.
In conventional digital cellular networks, position location capability can be provided by various time and/or phase measurement techniques. For example, in CDMA networks, one position determination approach used is Advanced Forward Link Trilateration (AFLT). Using AFLT, a mobile device may compute its position from phase measurements of pilot signals transmitted from a plurality of base stations. Improvements to AFLT have been realized by utilizing hybrid position location techniques, where the mobile device may employ an SPS receiver that can provide position information independent of the information derived from the signals transmitted by the base stations. Moreover, position accuracy can be improved by combining measurements derived from both SPS and AFLT systems using conventional techniques.
Furthermore, navigation devices often support popular and increasingly important SPS wireless technologies which may include, for example, the Global Positioning System (GPS) and/or a Global Navigation Satellite System (GNSS). Navigation devices supporting SPS may obtain navigation signals as wireless transmissions received from one or more transmitter equipped satellites that may be used to estimate geographic position and heading. Some navigation devices may additionally or alternatively obtain navigation signals as wireless transmissions received from terrestrial based transmitters to estimate geographic position and heading and/or include one or more on-board inertial sensors (e.g., accelerometers, gyroscopes, etc.) to measure an inertial state of the navigation device. Inertial measurements obtained from these on-board inertial sensors may be used in combination with or independent of navigation signals received from satellite and/or terrestrial based transmitters and/or inertial sensors on a vehicle (e.g., accelerometers, gyroscopes, odometers, etc.) to provide estimates of geographic position and heading.
However, conventional position location techniques based upon signals provided by SPS and/or cellular base stations may encounter difficulties when the mobile device is operating within a building and/or within urban environments. In such situations, signal reflection and refraction, multipath, and/or signal attenuation can significantly reduce position accuracy, and can slow the “time-to-fix” to unacceptably long time periods. These shortcomings may be overcome by having the mobile device exploit signals from other existing wireless networks (e.g., a wireless local area network (WLAN) that implements one or more 801.11x standards) to derive position information. Conventional position determination techniques used in other existing wireless networks may utilize a received signal strength indicator (RSSI) or round trip time (RTT) measurements derived from signals utilized within these networks and/or knowledge relating to time delays that the signals incur when propagating through various devices that make up the network. Such delays may be spatially variant due to, for example, multipath and/or signal interference. Moreover, such processing delays may change over time based upon the type of network device and/or the network device's current networking load.
Accordingly, conventional methods to augment position estimation using signals from SPS and/or cellular base stations and/or other existing wireless networks tend to be insufficient to effectively reduce error or location uncertainty. Moreover, using WLAN signals may incur additional costs in terms of hardware changes in wireless access points, time-consuming pre-deployment fingerprinting, and/or operational environment calibration, which may not be optimally exploited due to the existing limitations mentioned above. Further still, although sensor-assisted navigation techniques may use inertial sensors to overcome limitations associated with GPS and/or GNSS technology alone (e.g., when satellite signals may be unavailable in a parking garage or tunnel or severely degraded in urban canyons and other environments where sightlines to satellites may be blocked or subject to multipath propagation), existing sensor-assisted navigation techniques (e.g., dead reckoning techniques) typically advance a previous location fix according to known or estimated velocities and headings to calculate the current position and thereby navigate from the previous location fix. Furthermore, when navigating from absolute rest, existing sensor-assisted navigation techniques may not have any knowledge relating to an initial position until and/or unless GPS and/or GNSS signals can be suitably acquired and tracked, and moreover, an initial heading or other navigation state may be unavailable until sufficient vehicle movement has occurred to enable the inertial sensors to determine velocities or other suitable parameters that may indicate the heading. In other words, existing sensor-assisted navigation techniques tend to fall short in suitably providing an initial position estimate and navigation state at start-up because a previously determined position to advance may not exist and because an initially stationary device does not have any data available to estimate current velocities or headings (e.g., sensors that know wheel diameters and record wheel rotations and steering directions may not produce any relevant velocity or heading readings while in a stationary state).